Our study presents the first attempt to explore the effectiveness of generative prior in monocular 3D construction. Our framework assumes a pre-trained textured 3D GAN. In this study, we adopt ConvMesh [1]}, which is purely trained with 2D supervisions from single-view natural images. With the help of GAN prior, our goal is to recover the geometry and appearance of a 3D object from a monocular image and its associated mask. Unlike SMR [2]} that uses ground truth masks, our method takes silhouettes estimated by an off-the-shelf segmentation model [3]}.
